Pedestrian detection with an ensemble of localized features

Shaopeng Tang*, Satoshi Goto

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, a new human detection approach from still image is proposed. Two vector features are extracted from the image. Histogram of oriented Gradient feature represents the gradient information of human. Histogram of modified local binary pattern is extracted from images convolved with Gabor filter, as a feature vector to represent texture information. It can be seen as a supplement of gradient information. Different support vector machine classifiers are trained by each type of vectors. Finally, two classifiers are combined together for the final result by using the proposed integration method. Because two features contain different information, they have low error dependency and can get high detection rate. Experiment is performed in a large dataset and it shows that this method outperforms state-of-the-art approaches and other combinations of features.

本文言語English
ホスト出版物のタイトルProceedings - IEEE International Symposium on Circuits and Systems
ページ2838-2841
ページ数4
DOI
出版ステータスPublished - 2009
イベント2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei
継続期間: 2009 5月 242009 5月 27

Other

Other2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
CityTaipei
Period09/5/2409/5/27

ASJC Scopus subject areas

  • 電子工学および電気工学

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